1. Field of the Invention
This invention pertains generally to in-situ detection of soil properties, and more particularly to in-situ detection of soil properties with FTIR/ATR spectroscopy.
2. Description of Related Art
Precision farming, a technique which involves managing agricultural inputs and outputs on a site-specific basis, has received much attention over the last decade due to its potential to decrease inputs, such as fertilizer costs, and to increase yields. This technique attempts to use all available information across the field, such as nutrient levels, moisture contents, pH, texture, etc., to manage nutrients on a site-specific basis.
Site-specific crop management (SSCM) is based on a system-engineering approach to crop production where inputs are applied on an “as needed basis”, which is made possible by recent innovations in information and technology such as microcomputers, geographic information systems, positioning technologies (Global Positioning System, GPS), and automatic control of farm machinery (Robert et al., 1994). SSCM combined with variable-rate application (VRA) allows one to apply the right amount of fertilizer at the correct location in the field. Experiments conducted at the University of Idaho showed that reductions in fertilizer application amounts of 25% due to variable rate application were obtained with no decrease in yield potential (Fisher et al., 1993). Farmers typically apply uniform fertilizer amounts in excess of what the crops need to prevent yield loss due to nutrient deficiency. Applying lower fertilizer amounts to areas within a field of limited yield potential can lead to savings in fertilizer costs as well as reduced leaching of nitrate into the groundwater.
One of the main obstacles to implementing precision farming techniques is the absence of accurate and easy-to-use soil sensors to gather information about a field.
One of the main obstacles to implementing precision farming techniques is the absence of inexpensive yet accurate methods of gathering information about a field. Soil properties such as nitrogen and moisture levels vary significantly over a growing season and need to be monitored frequently in order to create accurate management maps. There are several methods of obtaining information about a field. These include aerial photographs, satellite imagery, soil electrical conductivity sensors, yield maps, and intensive soil survey data (Franzen and Cihacek, 1998). Soil survey data allow for the most complete representation of a field. However, this method requires significant amounts of time and labor. Due to the interaction of soil properties, extensive soil sampling is often required in order to obtain an accurate representation of the field at a given point in time. Soil components differ in chemical characteristics; further complicating field studies. Nitrate is a highly mobile ion and easily leaches from the soil when moisture is applied. In contrast, soil pH, phosphorus and organic matter levels tend to be more stable with time and do not require such frequent sampling. There are commercial electrical resistance/conductivity sensors currently available for in-situ measurements of soil pH but none are available for accurate determination of soil nitrate, phosphorus, or organic matter concentrations.
Nitrogen is an important nutrient for crop production. Together with water and sunlight, nitrogen is one of the key ingredients for plant growth. Nitrate is believed to be the preeminent form of nitrogen available to plants (Adsett and Zoerb, 1991). As stated earlier, nitrate, which is an anion, moves with the waterfront through the soil and is therefore susceptible to leaching. Denitrification is the conversion of nitrate to a gaseous form that is lost to the environment. This process occurs in soils with warm temperatures, moist conditions, and a near neutral pH. There are two major sources of nitrogen in agricultural fields: animal manure and inorganic fertilizers. These two sources account for the majority of the nitrogen in fields and consequently lead to leaching problems. Weather plays an important role in the timing of fertilizer application. Large amounts of fertilizer are usually applied when fields are dry enough to allow machinery traffic. Due to the complex behavior of the nitrate molecule, nitrate losses are inevitable with this method. Applying nitrogen fertilizer on an “as needed” basis rather than using a single application has both environmental and economic benefits (Francis and Piekielek, 2004).
Knowing the nitrate variability across a field could allow one to apply the site-specific amount of fertilizer for the given area and prevent over-applying, which can lead to nitrate leaching into groundwater resulting in health problems such as “blue-baby” syndrome and stomach cancer as well as environmental issues like algal bloom and greenhouse effect due to N2O. Site-specific-crop-management (SSCM) combined with variable-rate application allows one to apply the right amount of fertilizer at the correct location in the field.
In addition to nitrogen, phosphorus is also an essential nutrient for plant growth. Measuring phosphorus in soils is not an easy task. The soluble fraction of phosphorus in soils is usually very low, often estimated to be on the order of 0.1 ppm or less (S. Pettygrove, personal communication, 11 Nov. 2005). For this reason, soluble phosphorus is not a very reliable indicator of plant-available phosphorus. As in most soil nutrient analyses, soil pH is very important for the determination of phosphorus concentration. In alkaline conditions, with pH above approximately 7.5, phosphorus forms insoluble carbonates. In acidic conditions, with pH below 6 or so, phosphate will be sorbed onto iron, aluminum, and manganese oxide surfaces to form precipitates. Phosphorus just does not remain in solution for long in soils.
The desired method of soil phosphorus analysis depends on the pH of the soil. For acidic conditions, the Bray method (Diamond, 1995) is used to determine PO4-P amounts. This involves extracting the PO4-P using a dilute acid fluoride solution. For alkaline conditions, the Olsen-P method (Olsen, 1982) is commonly used. This test uses 0.5 normal NaHCO3 as the extracting agent and allows for measurement of plant available phosphate in the 0 to 50 ppm PO4-P range. Modified methods are available to extend this range up to 200 ppm (Everett, 2005).
The third important component characterizing a soil condition is its organic matter content. Organic matter levels in soils are usually determined by measuring organic carbon amounts. This may be accomplished by burning the soil in a furnace or by wet chemistry techniques, both of which are not suited for in-situ measurement of organic matter. As with nitrogen and phosphorus, organic matter exists in different forms. When organic matter decomposes for some period of time, it forms a dark brown, spongy material called humus. Humus, in particular humic acid, provides many benefits to crop production, such as aiding in breaking down compacted soil particles, transferring micronutrients from the soil to the plant, and stimulating the development of microflora populations in soils (Bio Ag Technologies, 1999). Because of its negative charge due to its oxidized sites, humic acid is important for absorbing micronutrients.
Most researchers in the precision farming area are familiar with the attempts to develop techniques for measuring these three soil components (nitrogen, phosphorus, and organic matter) using near infrared (NIR) spectroscopy due to the availability of inexpensive instruments for this region of the electromagnetic spectrum. Typically, the near infrared spectra collected are related to concentrations through a data-processing technique such as multiple linear regression, partial least squares regression, principal components regression, or neural networks.
In most cases, the results are similar: the predictive ability of the model decreases significantly when applied to areas outside the calibration region. In other words, as soon as another component which wasn't in the calibration set affects the spectra, the ability of the calibration equation to predict a certain specie's concentration falls apart. A model is only as good as the original data used to define it, so no matter which transformations (partial least squares or principal components) are performed on near infrared spectra, the robustness of the model is always a concern. The primary reason for this lack of robustness is due to the absence of absorbance (or reflectance) peaks due to nitrate (or phosphorus or organic matter) that exist in the near infrared region. Unlike the mid-infrared region where fundamental vibration modes of molecules exist, the near infrared region contains much weaker peaks due to overtones and combinations of vibrational modes.
Recently, Fourier Transform Infrared (FTIR) Attenuated Total Reflection (ATR) spectroscopy has shown great promise for detecting low concentrations of nitrate. The FTIR/ATR technique applied to mid-infrared (mid-IR) spectra has advantages in terms of minimal sample preparation needed even for low nitrate contents (<10 ppm NO3—N) and increased sensitivity of nitrate peaks due to the fundamental modes of vibration of the nitrate molecule that occur in this region.
Linker et al. (2004) attempted to use principal component regression (PCR), partial least squares (PLS), and cross-correlation to predict nitrate contents in FTIR/ATR spectra of soil pastes. (Linker, R., A. Kenny, A. Shaviv, L. Singher, and I. Shmulevich. 2004. Fourier Transform Infrared-Attenuated Total Reflection Nitrate Determination of Soil Solutions Using Principal Component Regression, Partial Least Squares, and Cross-Correlation. Applied Spectroscopy 58(5):516-520, herein incorporated by reference in its entirety). Linker et al. experimented with eight soils ranging in NO3—N concentration from 0 to 1000 ppm. Three of the soils were calcareous soils, containing large amounts of carbonate. When three calcareous soils were not included, they obtained the best results using PLS (4 components, standard error of 32 ppm NO3—N), followed by PCR (7 components, 32 ppm NO3—N), and the worst results with cross correlation with reference libraries (using 6 spectra, 35 ppm NO3—N). When calcareous soils were included in the analysis, the standard errors increased approximately two-fold.